Utilizando o algoritmo das projeções sucessivas para poda de máquinas de aprendizado extremo (Pruning Extreme Learning Machines Using the Successive Projections Algorithm)

Diego Parente Mesquita (diego@diegoparente.com)1, Joao Gomes (jpaulo@lia.ufc.br)1, Leonardo Ramos Rodrigues (leonardorrodrigues@gmail.com)2, Roberto Kawakami Galvão (kawakami@ita.br)3


1Universidade Federal do Ceará
2IAE
3Intituto Tecnológico de Aeronáutica

This paper appears in: Revista IEEE América Latina

Publication Date: Dec. 2015
Volume: 13,   Issue: 12 
ISSN: 1548-0992


Abstract:
Extreme Learning Machine (ELM) is a recently proposed machine learning method with successful applications in many domains. The key strengths of ELM are its simple formulation and the reduced number of hyper-parameters. Among these hyper-parameters, the number of hidden nodes has significant impact on ELM performance since too few/many hidden nodes may lead to underfitting/overfitting. In this work, we propose a pruning strategy for ELM using the Successive Projections Algorithm (SPA) as an approach to automatically find the number of hidden nodes. SPA was originally proposed for variable selection. In this work, it was adapted in order to be used to prune ELMs. The proposed method was compared to the Optimally Pruned Extreme Learning Machine algorithm (OP-ELM), which is considered as a state of the art method. Real world datasets were used to assess the performance of the proposed method for regression and classification problems. The application of the proposed model resulted in much simpler models with similar performance compared to the OP-ELM. For some classification instances, the performance of the proposed method outperformed the OP-ELM method.

Index Terms:
neural networks, exterme learning machines, prunning techniques   


Documents that cite this document
This function is not implemented yet.


[PDF Full-Text (253)]